Methods and apparatus to determine a duration of media presentation based on tuning session duration

ABSTRACT

Methods, apparatus, systems, and articles of manufacture are disclosed to determine a duration of media presentation based on tuning session duration. Example apparatus a receiver to obtain a first tuning session duration indicative of an amount of time between channel changes of a first media presentation device at a first media presentation location, a presentation session estimator to select a model from storage, the model selected based on a match of the first tuning session duration and a second tuning session duration, the model including a relation between the second tuning session duration and a first presentation session duration of media presented on a second media presentation device at a second media presentation location, and estimate a second presentation session duration of media presented within the first tuning session duration based on the model.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 16/860,026, filed Apr. 27, 2020, which is a continuation of U.S.patent application Ser. No. 15/990,729, filed May 28, 2018, now U.S.Pat. No. 10,638,177, which is a continuation of U.S. patent applicationSer. No. 15/011,455, filed Jan. 29, 2016, now U.S. Pat. No. 9,986,272,which claims the benefit of U.S. Provisional Patent Application No.62/239,126, entitled “METHODS AND APPARATUS TO DETERMINE A DURATION OFMEDIA PRESENTATION BASED ON TUNING SESSION DURATION” filed Oct. 8, 2015.U.S. patent application Ser. No. 16/860,026, U.S. patent applicationSer. No. 15/990,729, U.S. patent application Ser. No. 15/011,455, andU.S. Provisional Patent Application No. 62/239,126 are herebyincorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to media audience measurement, and,more particularly, to methods and apparatus to determine a duration ofmedia presentation based on tuning session duration.

BACKGROUND

Determining a size and demographics of an audience of a mediapresentation helps media providers and distributors schedule programmingand determine a price for advertising presented during the programming.In addition, accurate estimates of audience demographics enableadvertisers to target advertisements to certain types and sizes ofaudiences. To collect these demographics, an audience measurement entityenlists a plurality of media consumers (often called panelists) tocooperate in an audience measurement study (often called a panel) for apredefined length of time. The media consumption habits and demographicdata associated with these enlisted media consumers are collected andused to statistically determine the size and demographics of the entireaudience of the media presentation. In some examples, this collecteddata (e.g., data collected via measurement devices) may be supplementedwith survey information, for example, recorded manually by thepresentation audience members.

The process of enlisting and retaining participants for purposes ofaudience measurement is often a difficult and costly aspect of theaudience measurement process. For example, participants are typicallycarefully selected and screened for particular characteristics so thatthe population of participants is representative of the overallpresentation population. Additionally, the participants are required toperform specific tasks that enable the collection of the data, such as,for example, periodically self-identifying while consuming mediaprogramming.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which mediapresentation information is collected from media presentation locationsand is analyzed by an example collection facility to determine durationsof media presentation in accordance with the teachings of thisdisclosure.

FIG. 2 is a block diagram of an example implementation of the dataadjuster of FIG. 1 .

FIGS. 3-5 are flowcharts illustrating example machine readableinstructions that may be executed to implement the example data adjusterof FIGS. 1 and/or 2 .

FIG. 6 is an example table of measurement data collected by the examplelocal people meter of FIG. 1 .

FIGS. 7A-7C are tables of example tuning session data and presentationsession data collected by the example local people meter of FIG. 1 inaccordance with the teachings of this disclosure.

FIGS. 8A-8E are tables of statistical models generated by the dataadjuster of FIGS. 1 and/or 2 based on data from the local people metersof FIG. 1 in accordance with the teachings of this disclosure.

FIG. 9 is an example graph illustrating expected total presentationsession durations generated by the generated by the data adjuster ofFIGS. 1 and/or 2 based on received tuning session durations inaccordance with the teachings of this disclosure.

FIG. 10 is a block diagram of an example processing system capable ofexecuting the example machine readable instructions of FIGS. 3-5 toimplement the example data adjuster of FIGS. 1 and/or 2 .

DETAILED DESCRIPTION

Audience measurement entities seek to understand the composition andsize of audiences of media, such as television programming. Suchinformation allows audience measurement entity researchers to, forexample, report advertising delivery and/or targeting statistics toadvertisers that target their media (e.g., advertisements) to audiences.Additionally, such information helps to establish advertising pricescommensurate with audience exposure and demographic makeup (referred toherein collectively as “audience configuration”). One way to gathermedia presentation information is to gather media presentationinformation from media output devices (e.g., gathering televisionpresentation data from a set-top box (STB) connected to a television).As used herein media presentation includes media output regardless ofwhether or not an audience member is present (e.g., media output by amedia output device at which no audience is present, media exposure toan audience member(s), etc.).

A media presentation device (e.g., STB) provided by a service provider(e.g., a cable television service provider, a satellite televisionservice provider, an over the top service provider, a music serviceprovider, a movie service provider, a streaming media provider, etc.) orpurchased by a consumer may contain processing capabilities to monitor,store, and transmit tuning data (e.g., which television channels aretuned on the media presentation device at a particular time) to anaudience measurement entity (e.g., The Nielsen Company (US), LLC.) toanalyze media presentation activity. The tuning data is based on datareceived from the media presentation device while the media presentationdevice is on (e.g., powered on, switched on, and/or tuned to a mediachannel, streaming, etc.). However, tuning data may include extraneousdata that may not accurately reflect media presentation when, forexample, the media presentation device is configured to output media viaa media output device (e.g., a television), but the media output deviceis turned off, not receiving the media from the media presentationdevice, etc. For example, tuning data may include data related to a STBthat outputs television media via a television while the television isoff, disconnected, turned to input other than the STB, etc. In anotherexample, the tuning data collected by the media presentation device maynot accurately reflect media actually exposed to an audience when themedia presentation device is attempting to present the media but noaudience members are present (e.g., a STB and/or a television is onand/or presenting media while no person is present to consume themedia). To develop a more accurate estimation of the actual mediapresentation by the media presentation device, methods and apparatusdisclosed herein analyze measurement data (e.g., tuning data) collectedfrom media presentation devices (that may inaccurately reflect the mediaactually presented to an audience).

To determine aspects of media presentation data (e.g., which householdmember is currently consuming a particular media and the demographics ofthat household member), market researchers may perform audiencemeasurement by enlisting a subset media consumers as panelists.Panelists are audience members (e.g., household members, users,panelists, etc.) enlisted to be monitored, who divulge and/or otherwiseshare their media activity and/or demographic data to facilitate amarket research study. An audience measurement entity typically monitorsmedia presentation activity (e.g., viewing, listening, etc.) of thepanelist members via audience measurement system(s), such as a meteringdevice(s) and/or a local people meter (LPM). Audience measurementtypically include determining the identity of the media being presentedon a media output device (e.g., a television, a radio, a computer,etc.), determining data related to the media (e.g., presentationduration data, timestamps, channel data, etc.), determining demographicinformation of an audience, and/or determining which members of ahousehold are associated with (e.g., have been exposed to) a mediapresentation. For example, an LPM in communication with an audiencemeasurement entity communicates audience measurement (e.g., metering)data to the audience measurement entity. As used herein, the phrase “incommunication,” including variances thereof, encompasses directcommunication and/or indirect communication through one or moreintermediary components and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic or aperiodicintervals, as well as one-time events.

In some examples, metering data (e.g., including media presentationdata) collected by an LPM or other meter is stored in a memory andtransmitted via network, such as the Internet, to a datastore managed bythe audience measurement entity. Typically, such metering data iscombined with additional metering data collected from a plurality ofLPMs monitoring a plurality of panelist households. Example disclosedherein process the collected and/or aggregated metering data todetermine model(s) based on a period of time between channel changes(referred to herein as tuning sessions). The metering data and/or themodel(s) may include, but are not limited to, a number of minutes ahousehold media presentation device was tuned to a particular channel, anumber of minutes a household media presentation device was used (e.g.,consumed) by a household panelist member and/or a visitor (e.g., apresentation session), demographics of the audience (which may bestatistically projected based on the panelist data), informationindicative of when the media presentation device is on or off, and/orinformation indicative of interactions with the media presentationdevice (e.g., channel changes, station changes, volume changes, etc.).As used herein a channel may be a tuned frequency, selected stream, anaddress for media (e.g., a network address), and/or any other identifierfor a source and/or carrier of media.

In an effort to transform collected tuning data from media presentationdevices (e.g., STBs) into media presentation data (e.g., to account fordata including when the media output device is off or not used and/orwhen an audience member is not present), examples disclosed hereinestimate presentation data from collected tuning data based on modelsdetermined from the metering data received from LPMs. Examples disclosedherein include determining a first tuning session based on a period oftime between channel changes of a first media presentation device. Suchexamples further include determining first presentation session datawithin the determined first tuning session. Such examples furtherinclude determining a model relating the first tuning session with thefirst presentation session data. Such examples further includedetermining a second tuning session for tuning data from a second mediapresentation device. Such examples further include selecting the modelfor the second tuning session, based on a match of a first duration ofthe second tuning session and a second duration associated with themodel. Such examples further include estimating second presentationsession data for the second tuning session based on the model.

FIG. 1 is a block diagram of an example environment 100 in which tuningdata is collected from an example media presentation location 110 and isanalyzed by an example collection facility 114 to estimate presentationsession for tuning sessions within the tuning data. The exampleenvironment 100 includes a first example media presentation location102, example media output devices 104, an example LPM 106, example mediapresentation devices 108, the second media presentation location 110, anexample network 112, the example collection facility 114, an exampledata adjuster 120, an example tuning storage 116, and an examplemetering storage 118. According to the illustrated example, thecollection facility 114 collects audience measurement (e.g., metering)data from the example LPM 106. The example data adjuster 120 createsmodel(s) based on the collected metering data. The example data adjuster120 uses the models to estimate presentation sessions based on tuningdata from the example media presentation device 108. For example, thedata adjuster 120 of the illustrated example estimates mediapresentation session(s) for tuning sessions received from the examplemedia presentation device 108 of the example media presentation location110 that does not include a device to collect and/or send mediapresentation data (e.g., media presentation locations that do notinclude the example LPM 106 to the collection facility 104).

The example first media presentation location 102 is a location that hasbeen statistically selected to develop media ratings data for apopulation/demographic of interest. According to the example of FIG. 1 ,person(s) of the household have registered with a metering device (e.g.,the example local people meter 106) and provided the demographicinformation. Alternatively, the first example media presentationlocation 102 may be additional and/or alternative types of environmentssuch as, for example, a room in a non-statistically selected household,a theater, a restaurant, a tavern, a retail location, an arena, etc. Insome examples, the environment 100 may include a plurality of firstmedia presentation locations 102 for which metering data is collected.

In the illustrated example of FIG. 1 , the first media presentationlocation 102 includes the example media output device 104. The examplemedia output device 104 of FIG. 1 is a television. Alternatively, themedia output device 104 may be any other type of device for outputtingmedia such as, for example, a radio, a computer monitor, a video gameconsole, and/or any other device capable of presenting media to a user.

The example LPM 106 is in communication with the example media outputdevice 104 to collect and/or capture signals emitted externally by themedia output device 104. The LPM 106 may be coupled with the mediaoutput device 104 via wired and/or wireless connection. The example LPM106 may be implemented in connection with additional and/or alternativetypes of media presentation devices such as, for example, a radio, acomputer monitor, a video game console, and/or any other device capableto present media to a user. The LPM 106 may be a portable people meter,a cell phone, a computing device, a sensor, and/or any other devicecapable of metering user exposure to media. The media presentationlocation 102 may include a plurality of LPMs 106. In such examples, theplurality of the LPMs 106 may be used to monitor media exposure formultiple users and/or media output devices 104.

In some examples, the example LPM 106 includes a set of buttons assignedto audience members to determine which of the audience members iswatching the example media output device 104. The LPM 106 mayperiodically prompt the audience members via a set of LEDs, a displayscreen, and/or an audible tone, to indicate that the audience member ispresent at the example first media presentation location 102 by pressingan assigned button. To decrease the number of prompts and, thus, thenumber of intrusions imposed upon the media consumption experience ofthe audience members, the LPM 106 prompts only when unidentifiedaudience members are located in the first media presentation location102 and/or only after the LPM 106 detects a channel change and/or achange in state of the media output device 104. In other examples, theLPM 106 may include at least one sensor (e.g., a camera, 3-dimensionalsensor, etc.) and/or be communicatively coupled to at least one sensorthat detects a presence of the user in the first example mediapresentation location 102. The example LPM 106 transmits metering datato a media researcher and/or a marketing entity. The example meteringdata includes the media presentation data (e.g., data related to mediapresented while the media output device 104 is on and a user ispresent). The metering data may further include a householdidentification, a tuner key, a presentation start time, a presentationend time, a channel key, etc., as further described in FIG. 6 .

The media presentation device 108 of the illustrated example of FIG. 1is installed by a service provider (e.g., cable media service provider,a radio frequency (RF) media provider, a satellite media serviceprovider, etc.) to present media to an audience member through theexample media output device 104. In the illustrated example of FIG. 1 ,the example media presentation device 108 is a STB. Alternatively, theexample media presentation device 108 may be an over the top device, avideo game counsel, a digital video recorder (DVR), a digital versatiledisc (DVD) player, a receiver, a router, a server, and/or any devicethat receives media from a service provider. In some examples, the mediapresentation device 108 may implement a DVR and/or DVD player. Theexample media presentation device 108 includes a unique serial numberthat, when associated with subscriber information, allows an audiencemeasurement entity, a marketing entity, and/or any other entity toascertain specific subscriber behavior information. Additionally, theexample media presentation device 108 transmits tuning data (e.g., datarelated to tuned channels while the media presentation device 108 is on)to the example collection facility 114. Although the example mediaoutput device 104, the example LPM 106, and the example mediapresentation device 108 in the first example media presentation location102 are separate devices, one or more of the media output device 104,the LPM 106, and/or the media presentation device 108 may be combined.

The example second media presentation location 110 includes the examplemedia output device 104 and the example media presentation device 108,but does not include the example LPM 106. Accordingly, mediapresentation data is not collected at the example second mediapresentation location 110. However, tuning data is collected by theexample media presentation device 108. Such tuning data includes datacollected by the media presentation device 108 (e.g., which channel themedia presentation device 108 was tuned to) but may not includepresentation session information from the example media presentationdevice 108 (e.g., information related to when the media output device104 is powered on and/or an audience member is present). Thereforetuning data from the example LPM 106 may be misleading. In someexamples, the second media presentation location 110 may include asecond plurality of media presentation locations 110.

Metering data from the example LPM 106 and/or tuning data from theexample media presentation device 108 is transmitted to the examplecollection facility 114 via the example network 112. The example network112 may be implemented using any type of public or private network suchas, but not limited to, the Internet, a telephone network, a local areanetwork (LAN), a cable network, and/or a wireless network. To enablecommunication via the network 112, the example media presentation device108 includes a communication interface that enables a connection to anEthernet, a digital subscriber line (DSL), a telephone line, a coaxialcable, or any wireless connection, etc.

The example collection facility 114 receives, processes, stores, and/orreports presentation data related to metering data received from the LPM106 and/or tuning data from the media presentation device 108periodically and/or upon a request by the collection facility 114. Insome examples, the collection facility 114 receives the tuning data froma service provider associated with the media presentation device 108instead of and/or in addition to obtaining the example tuning data fromthe example media presentation device 108.

According to the illustrated example, the collection facility 114 ishosted by an audience measurement entity. Alternatively the collectorfacility may be hosted by any other entity or may be co-hosted by anaudience measurement entity and another entity(ies). For example, tuningdata may be collected from the example media presentation devices 108 bya media provider (e.g., a cable television provider, a satellitetelevision provider, etc.) and metering data may be collected from theexample LPM(s) 106 by an audience measurement entity cooperating withthe media provider to gain access to the tuning data. The examplecollection facility 114 includes the example tuning storage 116 and theexample metering storage 118.

The example tuning storage 116 is a database that stores tuning datareceived from the example media presentation device 108 and the examplemetering storage 118 is a database that stores metering data from theexample LPM(s) 106. The example tuning storage 116 and metering storage118 may be implemented by any one of more of a database, a server,and/or any other data structure to store data. According to theillustrated example, the example tuning storage 116 and the examplemetering storage 118 are communicatively coupled with the first examplemedia presentation location(s) 102 and the second example mediapresentation location(s) 110 via the example network 112. Alternatively,the example tuning storage 116 and/or the example metering storage 118may receive data in any other manner (e.g., tuning data and/or mediapresentation data may be collected by a third-party and transferred tothe collection facility 114 via the network 112 or any other path).

The example data adjuster 120 processes metering data (e.g., meteringdata received from the metering storage 118) to create a tuningsession(s) (e.g., based on a period of time between channel changes) anda presentation session(s) (e.g., based on when the media was presentedby the media presentation device 108 on the media output device 104).The example data adjuster 120 integrates demographic data with thecompiled presentation data to generate demographic statisticalinformation. The data adjuster 120 of the illustrated example generatesmodels to estimate presentation session data for a received tuningsession received from the example media presentation devices 108. Whenthe example collection facility 114 receives tuning data from theexample media presentation devices 108 and/or from a service providerassociated with the media presentation devices 108, the example dataadjuster 120 estimates and reports presentation session data based on acomparison of the tuning data and the generated models, as furtherdescribed in FIG. 2 .

In operation, there are two steps to estimating presentation session(s)for tuning data received from the example media presentation device 108.The first step is a model generation step that includes generatingmodels based on determined tuning session(s) and presentation session(s)from metering data. The second step is a media presentation estimationstep that includes estimating presentation sessions for received tuningdata.

During the model generation step, the example LPM 106 collects meteringdata at the media presentation location 102. As previously described,the metering data includes data related to media presented to and/orexposed to audience members of the media presentation device 108. Insome examples, the metering data includes demographics for the users ofthe media output device 104, data related to the media presented by themedia presentation device 108, timestamps for the media exposure, datarelated to channel changes, data related to media output device 104on/off status, etc. The example LPM 106 transmits the metering data tothe example collection facility 114 via the example network 112 to bestored in the example metering storage 118. As previously described, themetering data is received (e.g., from the LPM 106) periodically and/orupon a request by the collection facility 114. Typically, multiple ofthe LPMs 106 associated with respective ones of the media presentationlocations 102 will send the metering data to the example meteringstorage 118.

The example data adjuster 120 analyzes the metering data from theexample metering storage 118 to create tuning sessions and presentationsessions based on the metering data. The data adjuster 120 determinestuning session(s) based on a period of time between channel changesindicated in the metering data. The example data adjuster 120 alsodetermines presentation session(s) for the determined tuning session(s)based on a time and/or date of when the media was actually viewed by auser (e.g., the media output device 104 was detected as being on and auser was determined to be present to view the media output device 104).After the tuning session(s) and the presentation session(s) aredetermined, the data adjuster 120 of the illustrated example createsand/or updates a model based on a duration(s) of the tuning session(s),as further described in conjunction with FIG. 2 .

During the media presentation estimation step, the media presentationdevice 108 collects tuning data related to which channel a mediapresentation device 108 is tuned to while the media presentation device108 is on. As previously described, the tuning data does not includepresentation session data (e.g., data related to media presented whilethe media output device 104 is on and a user is present). The examplemedia presentation device 108 transmits the tuning data to the exampletuning storage 116 of the example collection facility 114 via theexample network 112. As previously described, the tuning data isreceived (e.g., from the media presentation device 108) periodicallyand/or upon a request by the collection facility 114. In some examples,the tuning data may be collected by the service provider associated withthe media presentation device 108. In such examples, the serviceprovider may transmit the tuning data directly to the example collectionfacility 114 to be stored in the tuning storage 116.

The example data adjuster 120 determines a duration of a tuning sessionfrom the received tuning data. The example data adjuster 120 estimatespresentation session data for the tuning session based on the createdmodels. For example, the data adjuster 120 may estimate a 120-minutepresentation session based on receiving a 180-minute tuning session. Theexample data adjuster 120 generates reports based on the estimatedpresentation session data. The reports may be generated at preset times(e.g., hourly, daily, monthly, etc.) and/or may be initiated by userrequest. Additionally, the reports may include data from one or moremedia presentation locations (e.g., such as the first and second mediapresentation locations 102, 110). In some examples, the reports mayinclude demographic and/or other statistical information, as furtherdescribed in FIGS. 8A-9 .

FIG. 2 is block diagram of an example implementation of the example dataadjuster 120 of FIG. 1 to estimate presentation sessions for tuning databased on models generated from metering data. The example data adjuster120 includes an example metering receiver 202, and example tuningsession determiner 204, an example presentation session determiner 206,an example modeler 208, an example model storage 210, an example tuningdata receiver 212, an example duration determiner 214, an examplepresentation session estimator 216, and an example reporter 218.

The example metering receiver 202 of FIG. 2 receives metering data fromthe example LPM 106 and sends the received metering data to the exampletuning session determiner 204 for further processing. In some examples,the metering receiver 202 receives metering data from the examplemetering storage 118. In some examples, the metering receiver 202receives metering data from the example LPM(s) 106. In some examples,the metering receiver 202 receives metering data from both the examplemetering storage 118 and the example LPM(s) 106. The metering receiver202 may include a network adapter and/or server for receiving meteringdata from the example metering storage 118 and/or the example LPM(s) 106(e.g., via the example network 112) through a wired and/or wirelessconnection.

The example tuning session determiner 204 analyzes metering datareceived via the example metering receiver 202 to create a tuningsession(s) based on a period of time between channel changes.Alternatively, the tuning session determiner 204 may generate thecreated tuning session(s) based on a period of time between anyinteraction with the media output device 104, the LPM 106, and/or themedia presentation device 108. According to the illustrated example, theexample tuning session determiner 204 determines a new tuning sessionfor each channel change identified in the metering creates a new tuningsession. In this manner, a tuning session is representative of theperiod of time between each channel change. For example, if the meteringdata includes a first channel change at 4:00 PM and a second subsequentchannel change at 5:30 PM on the same day, the example tuning sessiondeterminer 204 creates a 90-minute tuning session representative of theperiod from 4:00 PM to 5:30 PM. Once a tuning session(s) has beendetermined from the metering data, the example tuning session determiner204 sends data for the determined tuning session(s) to the examplepresentation session determiner 206.

The example presentation session determiner 206 receives data for atuning session(s) received from the example tuning session determiner204 and further analyzes created tuning session(s) to determine apresentation session(s) within the tuning session(s). The presentationsessions are determined based on when the media presentation device 108is actually presenting media to an audience member (e.g., the examplemedia output device 104 is on and/or an audience member is present). Insome examples, the metering data may include user identifiersidentifying which user is located in the example media presentationlocation 102 while the example media output device 104 is on. In suchexamples, the presentation session determiner 206 may not credit aduration as a presentation session if an audience member is not presentwhile the media output device 104 is on. Once the presentationsession(s) is determined, the example presentation session determiner206 transmits the created tuning session data and the determinedpresentation session data to the example modeler 208.

The example modeler 208 creates and/or updates models based on tuningsession data and presentation session data received from the example.The example modeler 208 integrates the presentation session data in amodel with a corresponding tuning session length. For example, if theexample tuning session determiner 204 determines presentation sessiondata from a 500-minute tuning session, the example modeler 208 willstore the corresponding presentation session data in a 500-minute tuningsession model. In some examples, the example modeler 208 updates themodel based on the total presentation session for the tuning session.For example, if the 500-minute tuning session includes a totalpresentation session of 320 minutes, the example modeler 208 will updatethe 500-minute tuning session model to include the 320 minutepresentation session, as further described in FIG. 8A. In some examples,the example modeler 208 updates the model based on durations associatedwith the presentation session for the tuning session. For example, ifduring the 500 minute tuning session, there were two presentationsessions (e.g., from the 0 minute mark to the 200 minute mark and fromthe 380 minute mark to the 500 minute mark), the example modeler 208will update the 500-minute tuning session model to include data from theperiods of time (e.g., 0-200 minutes and 380-500 minutes) associatedwith the presentation sessions, as further described in FIG. 8D.Additionally, the example modeler 208 may update the model based onvarious conditional probabilities associated with the presentationsession(s), as further described in FIGS. 8B, 8C, 8E, and 9 . Once theexample modeler 208 has created and/or updated a model, the examplemodeler 208 stores the model in the example modal storage 210.

The example model storage 210 of FIG. 2 stores models created and/orupdated by the example modeler 208. In some examples, the model storage210 includes hardware, software, or firmware to store data locally inthe example data adjuster 120. Alternatively, the model storage 210 islocated outside the example data adjuster 120 (e.g., in a databaseand/or a cloud). The models stored in the example model storage 210 maybe updated (e.g., based on additional metering data) and/or used toestimate presentation sessions (e.g., based on the tuning data).

The example tuning data receiver 212 of FIG. 2 receives tuning data fromthe example media presentation device 108 and/or a service provider andsends the received tuning data to the example duration determiner 214for further processing. In some examples, the tuning data receiver 212receives metering data from the example tuning storage 116.Alternatively, the tuning data receiver 212 may receive metering datafrom the example media presentation device(s) 108. In some examples, thetuning data receiver 212 receives metering data from both the exampletuning storage 116 and the example media presentation device(s) 108. Thetuning data receiver 212 may include a network adapter and/or server forreceiving metering data from the example tuning storage 116 and/or theexample media presentation device(s) 108 (e.g., via the example network112) via a wired and/or wireless connection.

The example duration determiner 214 analyzes tuning data to determine aduration of a tuning session from the media presentation device 108. Aspreviously described, a tuning session is based on a period time betweenchannel changes of the media presentation device 108. The tuning datadoes not include presentation session data. To estimate accuratepresentation session for the tuning data, the example durationdeterminer 214 determines the duration of the tuning session so that anappropriate model may be retrieved to determine the estimate. Theexample duration determiner 214 transmits tuning data including thetuning session duration to the example presentation session estimator216 for further processing.

The example presentation session estimator 216 estimates presentationsession for tuning data received from the example tuning data receiver212 via the example duration determiner 214 based on presentationsession data from a model stored in the example model storage 210. Thepresentation session estimator 216 retrieves, from the example modelstorage 210, a model with a tuning session length that matchesdetermined tuning session duration determined by the example durationdeterminer 214. For example, if the tuning data received from theexample model storage 210 a 500 minute tuning session, the presentationsession estimator 216 retrieves the 500-minute tuning session model fromthe example memory 210. Since the tuning data does not differentiatebetween time when the media output device 104 is on and the media outputdevice 104 is off and/or when an audience member is present, the examplepresentation session estimator 216 estimates presentation sessions toaccount for periods of time when the media presentation device is on butthe media output device 104 is off and/or an audience member is notpresent. The example presentation session estimator 216 may estimateadditional presentation session data based on, for example, an initialpresentation session (e.g., the first presentation session in the tuningsession), a final presentation session (e.g., the last presentationsession in the tuning session, and/or a total presentation session(e.g., the total presentation minutes in the tuning session) based onthe data stored in the corresponding model. For example, thepresentation session estimator 216 may receive a 200-minute model(s)while estimating a presentation session for a 200-minute tuning session.The presentation session estimator 216 may estimate, based on the200-minute model, an initial tuning session of 60 minutes, an finalpresentation session of 30 minutes, and an total presentation sessionestimate of 90 minutes based on user and/or administrator settings. Insome examples, the settings may be based on statistical analysis (e.g.,expected value, weighted average, standard deviation, minimum and/ormaximum percentages of presentation sessions from the model(s) etc.).

In some examples, the example presentation session estimator 216 bins(e.g., groups) data from multiple models within a threshold range whenthe number of entries in a particular model does not satisfy a minimumthreshold number of entries. For example, if a 65-minute tuning durationis determined from tuning data and the 65-minute tuning session modeldoes not meet a threshold number (e.g., minimum) of entries, the examplepresentation session estimator 216 may group data from models of similartuning session length within a threshold range. For example, if thethreshold range is 4 minutes, the data from the 63-minute tuning sessionmodel, the 64-minute tuning session model, the 66-minute tuning sessionmodel, and the 67-minute tuning session model may be combined with thedata from the 65-minute tuning session model. In this manner, the numberof entries may be increased until the threshold number of entries issatisfied. In some examples, the threshold number of entries for a modeland the minimum threshold range may be set and/or adjusted by a userand/or an administrator.

The example reporter 218 of FIG. 2 generates reports of data received,determined, and/or generated by the example data adjuster 120. Theexample reporter 218 generates reports including media presentationsession data, the metering data from the example LPM 106, tuning datafrom the media presentation device 108, data relating models generatedby the example modeler 208, presentation session estimator 216 settings,and/or any other data relating to the LPM 106 and/or the mediapresentation device 108. The reports may include statistical analysisincluding conditional distributions, cumulative distributions, expectedvalues, etc. For example, the reports may illustrate that 15% of200-minute tuning sessions from media presentation devices 108 includeonly 120 minutes of presentation time, that 35% of the 200-minute tuningsession was being presented at the 158^(th) minute, that the expectedtotal presentation minutes for the 200-minute tuning session is 150minutes, etc. The reports may be preset and/or customized by a userand/or administrator to include information relevant to the user and/oradministrator.

While an example manner of implementing the example data adjuster 120 ofFIG. 1 is illustrated in FIG. 2 , one or more elements, processes and/ordevices illustrated in FIG. 2 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample metering receiver 202, the example tuning session determiner204, the example presentation session determiner 206, the examplemodeler 208, the example model storage 210, the example tuning datareceiver 212, the example duration determiner 214, the examplepresentation session estimator 216, the example reporter 218, and/or,more generally, the example the example data adjuster 120, of FIG. 2 maybe implemented by hardware, machine readable instructions, software,firmware and/or any combination of hardware, machine readableinstructions, software and/or firmware. Thus, for example, any of theexample metering receiver 202, the example tuning session determiner204, the example presentation session determiner 206, the examplemodeler 208, the example model storage 210, the example tuning datareceiver 212, the example duration determiner 214, the examplepresentation session estimator 216, the example reporter 218, and/or,more generally, the example the example data adjuster 120, of FIG. 2could be implemented by one or more analog or digital circuit(s), logiccircuit(s), programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one the example meteringreceiver 202, the example tuning session determiner 204, the examplepresentation session determiner 206, the example modeler 208, theexample model storage 210, the example tuning data receiver 212, theexample duration determiner 214, the example presentation sessionestimator 216, the example reporter 218, and/or, more generally, theexample the example data adjuster 120, of FIG. 2 is/are hereby expresslydefined to include a tangible computer readable storage device orstorage disk such as a memory, a digital versatile disk (DVD), a compactdisk (CD), a Blu-ray disk, etc. storing the software and/or firmware.Further still, the example data adjuster 120 of FIG. 2 may include oneor more elements, processes and/or devices in addition to, or insteadof, those illustrated in FIG. 2 , and/or may include more than one ofany or all of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the example data adjuster 120 of FIG. 2 are shown in FIG.3-5 . In the examples, the machine readable instructions comprise aprogram for execution by a processor such as the processor 1012 shown inthe example processor platform 1000 discussed below in connection withFIG. 10 . The program may be embodied in software stored on a tangiblecomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), a Blu-ray disk, or a memoryassociated with the processor 1012, but the entire program and/or partsthereof could alternatively be executed by a device other than theprocessor 1012 and/or embodied in firmware or dedicated hardware.Further, although the example program is described with reference to theflowcharts illustrated in FIGS. 3-5 , many other methods of implementingthe example data adjuster 120 of FIG. 2 may alternatively be used. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 3-5 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any period (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 3-5 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any period (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

The example machine readable instructions illustrated in FIG. 3 may beexecuted to cause the example data adjuster 120 of FIG. 2 to create amodel based on metering data and determine presentation session datafrom tuning data of the example media presentation device(s) 108 (e.g.,the model generation step) in conjunction with FIGS. 7-9 .

At block 300, the metering receiver 202 receives metering data from theexample LPM 106. As previously described, the metering data containsdetailed media exposure data for the example media presentation location102 (e.g., for media from the example media presentation device 108output by the example media output device 104. Example metering data isillustrated and further described in FIG. 6 . At block 302, the tuningsession determiner 204 creates a tuning session based on a period oftime between channel changes. Alternatively, the example tuning sessiondeterminer 204 may create a tuning session based on any other events atthe example media presentation location (e.g., a volume change, adetected user presence, etc.). Once the tuning session has been created,the example presentation session determiner 206 determines presentationsessions within the tuning session (block 304). Alternatively,presentation sessions may be determined prior to or in parallel with thecreation of the tuning session. An example of presentation sessionswithin a tuning session is illustrated and further described in FIGS.7A-7C.

At block 306, the example modeler 208 adds the presentation session datato a first set of models based on the tuning session length. Forexample, based on the determined tuning sessions, the example modeler208 updates the example models by adding the presentation data to thefirst set of models (e.g., such as frequency distribution of totalpresentation time and frequency distribution of media presented at settimes, as further described in FIGS. 8A and 8D). For example, if thereare 50 minutes of total presentation session time for a 75-minute tuningsession, the example modeler 208 adds a count for the total 50 minutespresentation session to a frequency distribution model for a 75-minutetuning session, as further described in FIG. 8A. Additionally, if theexample modeler 208 determines that the 75-minute tuning sessioncontains two presentation sessions (e.g., from 0-30 minutes and from55-75 minutes), the example modeler 208 may update a 75-minute frequencydistribution model based on every minute of the two presentationsessions (e.g., adds a count at 0 minute bucket, at a 1 minute bucket,at a 2 minute bucket, . . . , at a 30 minute bucket, at a 55 minutebucket, . . . , at a 75 minute bucket), as further described in FIG. 8D.Once the example modeler 208 updates the first set of models, theexample modeler 208 updates a second set of models associated with thefirst set of models (block 308). For example, there may be variousmodels (e.g., such as models of conditional distribution of totalpresentation time, models of cumulative distribution of totalpresentation time, models of conditional distribution of media presentedat set times, models of conditional expected value, etc., as furtherdescribed in FIGS. 8B, 8C, 8E, and 9 ) that are calculated based on thefirst set of models. For example, a 75-minute conditional distributionmodel is based on a number of counts in one bucket divided by the totalnumber of counts. In such examples, the conditional probability for a20-minute tuning session may include 100 counts for a presentationsession totaling 15 minutes and the 20-minute tuning session may have atotal of 500 counts, therefore the conditional probability for a 15minute total presentation session based on a 20-minute tuning session is20% (e.g., 100/500). However if additional metering data is received,the conditional data is calculated based on updates to the first set ofmodels. For example, if 500 more 20-minute tuning sessions are added tothe first set of models and none of the 500 20-minute tuning sessionsinclude 15-minute total presentation sessions, the conditionalprobability for a 15-minute total presentation session would lower to10% (e.g., 100/1000). In such examples, the presentation session data isfirst added to the first set of models and then the second set of modelsmay be updated (e.g., re-calculated) based on the updated first set. Atblock 310, once the models have been updated, the models are stored inthe example model storage 210 to be used by the presentation sessionestimator 216, as further described in FIG. 4 .

The example machine readable instructions illustrated in FIG. 4 may beexecuted to cause the example data adjuster 120 of FIG. 2 to estimatepresentation sessions from tuning data from the example mediapresentation device 108 (e.g., the media presentation estimation step).

At block 400, the example tuning data receiver 212 receives tuning datafrom the example media presentation device 108. As previously described,the tuning data includes data relating to which channel the mediapresentation device 108 was tuned to while the media presentation device108 is on. Tuning data may be inaccurate because tuning data assumesthat the media output device 104 is on and a viewer is present wheneverthe media presentation device is on. Therefore, tuning data does notprovide presentation session data (e.g., data related to when the mediaoutput device 104 is on and a user is present) within a tuning session.

At block 402, the example duration determiner 214 determines a durationof a tuning session based on the tuning data. Once the duration thetuning session has been determined, the example presentation sessionestimator 216 retrieves a corresponding model from the example modelstorage 210 (block 404). Since the example models are divided by tuningsession durations, the presentation session estimator 216 retrieves amodel that corresponds to (e.g., matches with) the duration the receivedtuning session. At block 406, the example presentation session estimator216 estimates presentation session data (e.g., a total estimatedpresentation session, a period for a presentation session at thebeginning and/or end of the tuning session, and/or any other data basedon the stored models as further described in FIGS. 8A-E) based on usersettings. For example, a user may create setting for an initialpresentation session based a when the total percentage of users in amodel drops below 80%. In such examples, if a 10 minute tuning sessionis received from the example tuning data receiver 212, the presentationsession estimator 216 receives a 10-minute model (e.g., such as theconditional distribution of media presented at set times model of FIG.8E). Since the 4^(th) minute of the model of FIG. 8E is the first timethat the conditional percentage drops below 80% (e.g., at the 4^(th)minute it is 75%), the presentation session estimator 216 estimates aninitial presentation session from the 0^(th) minute to the 3^(rd)minute. As previously described, the user settings may be preset ofconfigured based on user and/or administrator preferences. At block 408,the example reporter 218 generates a report including the estimatedpresentation session data. Additionally, the report may include thetuning data, the metering data, demographic data, any and/or all of thestored models, and/or any other data related to the LPM 106 and/or themedia presentation device 108. As previously described, the datareported on the reporter may be preset of customized.

The example machine readable instructions illustrated in FIG. 5 includealternative instructions to cause the example data adjuster 120 of FIG.2 to estimate presentation sessions from tuning data from the examplemedia presentation device 108. The example machine readable instructionscause the example data adjuster 120 of FIG. 2 to bin (e.g., group)models based on tuning session durations.

At block 500, the example tuning data receiver 212 receives tuning datafrom the example media presentation device 108. As previously described,the tuning data includes data relating to which channel the mediapresentation device 108 was tuned to while the media presentation device108 is powered on. Tuning data may be inaccurate because tuning dataassumes that the media output device 104 is on and a viewer is presentwhenever the media presentation device is on. Therefore, tuning datadoes not provide presentation session data (e.g., data related to whenthe media output device 104 is on and a user is present) within a tuningsession.

At block 502, the example duration determiner 214 determines a durationof a tuning session based on the tuning data. Once the duration thetuning session has been determined, the example presentation sessionestimator 216 retrieves a corresponding model from the example modelstorage 210 (block 504). Since example the models are divided by tuningsession durations, the presentation session estimator 216 retrieves amodel that corresponds to (e.g., matches with) the duration the receivedtuning session.

At block 506, the presentation session estimator 216 determines if theobtained model exceeds a minimum number of entries. The minimum numberof entries may be predetermined and/or based on user and/oradministrator preferences. If a model has a limited number of entries(e.g., small sample size), the presentation session estimator 216 mayinaccurately estimate presentation session data. As previouslydescribed, the example presentation session estimator 216 may bin (e.g.,group) similar models together to increase the number of entries abovethe minimum number of entries. If the model does exceed the minimumnumber of entries, the example presentation session estimator 216estimates presentation session data (e.g., a total estimatedpresentation session, a period for a presentation session at thebeginning and/or end of the tuning session, and/or any other data basedon the stored models as further described in FIGS. 8A-E) (block 508)based on user settings. For example, a user may create setting for aninitial presentation session based a when the total percentage of usersin a model drops below 80%. In such examples, if a 10 minute tuningsession is received from the example tuning data receiver 212, thepresentation session estimator 216 receives a 10-minute model (e.g.,such as the conditional distribution of media presented at set timesmodel of FIG. 8E). Since the 4^(th) minute of the model of FIG. 8E isthe first time that the conditional percentage drops below 80% (e.g., atthe 4^(th) minute it is 75%), the presentation session estimator 216estimates an initial presentation session from the 0^(th) minute to the3^(rd) minute. As previously described, the user settings may be presetof configured based on user and/or administrator preferences.

If the model does not exceed the minimum number of entries, the examplepresentation session estimator 216 estimates presentation session databased on the model and data from other models within a thresholdduration range (block 510). In this manner, the example presentationsession estimator 216 can increase the number of entries by gatheringdata from models with similar tuning session durations. For example, ifa threshold range is 5 minutes and a 30-minute tuning session model doesnot meet the minimum number of entries, the presentations sessionestimator 216 may combine entries from the 28-minute tuning sessionmodel, the 29-minute tuning session model, the 30-minute tuning sessionmodel, the 32-minute tuning session model, and the 33-minute tuningsession model. In some examples, the presentation session estimator 216may add entries from one model at a time until the minimum threshold ismet. The threshold range and/or the minimum number of entries may bepreset and/or based on user and/or administrator preferences. Once, theminimum threshold is met, the example presentation session estimator 216estimates presentation session data based on the binned (e.g., grouped)models (e.g., a total estimated presentation session, an duration for apresentation session at the beginning and/or end of the tuning session,and/or any other data based on the stored models as further described inFIGS. 8A-E). At block 512, the example reporter 218 generates a reportincluding the estimated presentation session data. Additionally, thereport may include the tuning data, the metering data, demographic data,any and/or all of the stored models, presentation session data prior tobinning, and/or any other data related to the LPM 106 and/or the mediapresentation device 108.

FIG. 6 is an illustration of example metering data 600 from the exampleLPM 106. The example metering data 600 includes a householdidentification (ID) 602, a tuner key 604, a start presentation time 606,an end presentation time 608, a channel key 610, a genre 612, apresentation weight date key 614, a valid data flag 616, and a source618.

The example household ID 602 of FIG. 6 identifies which example mediapresentation location 102 transmitted the metering data 600. In thisexample, there is one household ID 602, namely ‘30006.’ However, theremay be many household IDs from various media presentation locations 102within the metering data 600. The example tuner key 604 is anidentification number for the media output device 104. Since there maybe the media presentation location 102 with multiple media presentationdevices 102, the tuner key 604 identifies which media output device 104was being used. The example start time 606 is a timestamp based on astart of a presentation session (e.g., when the media presentationdevice 108 was actually presenting media on the media output device104). The example end time 608 is a timestamp based an end of apresentation session. The example channel key 610 identifies a channeltuned by the media presentation device 108. The example genre 612identifies the genre of the media tuned to on the media presentationdevice 108 during the presentation session. The example presentationweight date key 614 is a code representative of a date of the end time608. The example valid data flag 616 is a Boolean value that identifieswhether the metering data is valid. The metering data may not be validif there is an error in the metering data (e.g., the metering data iscorrupted, the metering data is missing information, etc.). The examplesource 618 identifies a source (e.g., a videocassette recorder (VCR),DVD, cable, antenna, video game counsel, etc.) of the media presentationdevice 618. The source 618 may change if, for example, the user iswatching a DVD. Additionally, the metering data 600 may containadditional columns for data related to other aspects of audience memberdata. For example, the metering data may contain data identifyingwhether or not a user(s) is present, demographics relating to theuser(s), and/or an identifier for the user(s) present during apresentation session. Alternatively, the metering data may only displaydata while a user is present and omit any data while the user is notpresent. For example, the example LPM 106 may adjust the examplemetering data 600 so that the metering data 600 does not include datafrom time durations when a user is not present.

When the example metering data 600 of FIG. 6 is received by the examplecollection facility 114 from the example LPM 106, the example tuningsession determiner 204 tuning sessions based on a period time betweenchannel changes by analyzing the metering data 600. The example columns620 represent data from a period time between channel changes. In thisexample, the tuning session determiner 204 identifies the examplecolumns 620 as an example tuning session as further described in inFIGS. 7A-C.

FIGS. 7A-C illustrate an example of determining a tuning session andpresentation sessions within the tuning session based on the examplemetering data 600 of FIG. 6 . In the illustrated example, the tuningsession determiner 204 determines tuning session based on a period oftime between channel changes. FIG. 7A displays the columns 620 from theexample metering data 600 of FIG. 6 . FIG. 7B displays information thatmay be extracted from the example columns 620 to determine the tuningsession and the presentation sessions. For example, based on theinformation from the example columns 620, the presentation sessiondeterminer 206 determines that at time 00:25:00 media output device‘186242092’ from household ‘50006’ was turned off after watching achannel associated with ‘294984.’ At time 00:34:00, the media outputdevice ‘186242092’ was turned on and the channel was changed to achannel associated with a channel key ‘2875552.’ At time 02:26:00, themedia output device ‘186242092’ was turned off. At time 04:52:00, themedia output device ‘186242092’ was turned back on while remaining onthe channel associated with the channel key ‘2875552.’ At time 05:46:00,the media output device ‘186242092’ was turned off. At time 20:50:00,the media output device ‘186242092’ was turned back on while remainingon the channel associated with the channel key ‘2875552.’ At time21:35:00, the media output device ‘186242092’ was turned off. At time22:41:00, the media output device ‘186242092’ was turned back on and thechannel was changed to a channel associated with the channel key‘294984.’

FIG. 7C illustrates an example tuning session and example presentationsessions determined for the metering data from columns 620 of FIG. 7A.Since the channel was changed at 00:34:00 and then again at 22:41:00,the example tuning session determiner 204 generates an example tuningsession of 1,327 minutes (e.g., the period of time between channelchanges). Once the tuning session is created, the presentation sessiondeterminer 206 determines presentation sessions based on the periods oftime that media from the media output device ‘186242092’ was actuallypresented within the tuning session (e.g., the media output device‘186242092’ was on and a user was viewing the media output device‘186242092’). For example, the presentation session determiner 206analysis the start and end times from the metering data from columns 620to determine when the media output device 104 was on and when the mediaoutput device 104 was off. The presentation sessions only includeperiods of time while the media output device 104 is on. In someexamples, the metering data in columns 620 may only include data when auser is present. In such examples, the presentation sessions are basedon when the media output device 104 is on. In some examples, themetering data in columns 620 may include additional data such as datarelated to the presence of audience members. In such examples, thepresentation session determiner 206 may need to determine if, and/orwhich, audience members are present while the media output device 104 ison. Based on the example columns 620, the presentation sessiondeterminer 206 determines that the presentation session periods are00:34:00-02:56:00 (e.g., 142 minutes), 04:52:00-0:5:46:00 (e.g., 54minutes), and 20:50:00-21:35:00 (e.g., 45 minutes). The totalpresentation time for the 1,327 minute tuning session is 238 minutes(e.g., 142+54+42=238). In this example, once the example presentationsession determiner 206 determines presentation session data based on thecreated tuning session, the example modeler 208 adds the presentationsession data to a model corresponding to a tuning session duration 1,327minutes, as further described in FIGS. 8A-8E.

FIGS. 8A-E display example models displaying various distributions basedon presentation session data for an example 10-minute tuning session.The example models of 8A-E are based on a total of 5963 10-minute tuningsessions collected from metering data of the example LPM 106 in FIG. 2 ,as previously described in FIGS. 7A-C.

FIG. 8A displays an example model of an example frequency distributionof total presentation time based on the gathered 10-minute tuningsession. Additionally, other models may be created for tuning sessionsof varying lengths (e.g., 1-minute tuning session, 5-minute tuningsession, 60-minute tuning session, 720-minute tuning session, etc.).Alternatively, one model may be generated with multiple rowsrepresenting multiple tuning session lengths.

The example model of FIG. 8A includes an example frequency distributionof presentation times 802 broken into one minute intervals for theexample 10-minute tuning session 800. In some examples, the presentationtimes 802 represent a range of times. For example, the examplepresentation time ‘0’ labeled 806 may include all time from 00:00:00 to00:00:59, 00:00:00 to 00:00:29, or any other range. The ranges may bepredetermined and/or may be customized by a user and/or anadministrator. Alternatively, the frequency distribution presentationtimes 802 may be broken into any duration of intervals (e.g., thirtysecond intervals, 2 minute intervals, 5 minute intervals, etc.).

To generate and/or update the example model of FIG. 8A, the examplecollection facility 114 of FIG. 1 collects metering data from theexample LPM 106. Once the example data adjuster 120 breaks the meteringdata into tuning sessions and presentation sessions, the example dataadjuster 120 populates the model(s) based on the tuning session data andpresentation session data. The example frequency distribution of FIG. 8Ais populated based on presentation session data from tuning sessions of10 minute length. Each of the 5,963 collected 10-minute tuning sessionsare represented in a presentation duration bucket based on the totalpresentation time of the tuning session (e.g., the amount time withinthe 10-minute tuning session 800 that media was presented). The exampleof FIG. 8A includes 112 instances of a total presentation time of 0minutes labeled 804 for a 10-minute tuning session 800, 242 instances ofa total presentation time of 1 minute labeled 808 for a 10-minute tuningsession 800, etc. As additional metering data is processed by theexample data adjuster 120, the example model is updated to represent theadditional monitored data.

Various statistical calculations (e.g., weighted average, standarddeviation, etc.) can additionally be determined by the example modeler208 based on the data from the frequency distribution of FIG. 8A. Forexample, an expected value (e.g., weighted average) may be calculatedusing the following formula:

$\overset{\_}{x} = \frac{\sum_{i = 1}^{n}{w_{i} \cdot x_{i}}}{\sum_{i = 1}^{n}w_{i}}$

Where x is the expected value, w_(i) is the number of instances inpresentation bucket i, and x_(i) is the number of presentation minutesof presentation bucket i.

The example model of FIG. 8A has an expected value of 6.58, as shownbelow:

$\frac{\begin{matrix}{{0(112)} + {1(242)} + {2(338)} + {3(370)} + {4(390)} +} \\{{5(490)} + {6(491)} + {7(781)} + {8(901)} + {9(903)} + {10(945)}}\end{matrix}}{5963} = 6.58$

The example expected value is the number of expected total presentationminutes given a 10-minute tuning session. In other words, given areceived 10-minute tuning session from a media presentation device, itis expected that a total of 6.58 minutes of the 10 minutes were actuallypresented to a user. The expected value for each tuning session lengthcan be plotted on a graph, as further described in FIG. 9 .

FIG. 8B displays an example model of an example conditional distributionof presentation time based on the example frequency distribution of FIG.8A. Additionally other models may be created for conditionaldistribution of presentation time for tuning sessions of varying lengths(e.g., 1-minute session, 5-minute tuning session, 60-minute tuningsession, 720-minute tuning session, etc.) Alternatively, one model maybe generated with multiple rows representing varying tuning sessionlengths.

The example model of FIG. 8B includes an example conditionaldistribution of presentation times 812 broken into one minute intervalsfor an example 10-minute tuning session 800. In some examples, thepresentation times 812 represent a range of times. For example, theexample presentation time ‘0’ labeled 814 may include all time from00:00:00 to 00:00:59, 00:00:00 to 00:00:29, or any other range. Theranges may be predetermined or may be customized by an administrator.Alternatively, the conditional distribution 812 may be broken into anyduration of intervals (e.g., thirty second intervals, 2 minuteintervals, 5 minute intervals, etc.).

Conditional distribution buckets contain conditional percentages basedon the frequency distributions of FIG. 8A. The conditional percentagesin the example conditional distribution buckets are calculated bydividing each frequency distribution bucket by a total number of tuningsessions modeled for a tuning session length. For example, theconditional distribution percentage for a 0-minute presentation session814 within a 10-minute tuning session 800 is calculating by dividing the112 instances of the 0-minute presentation session labeled 806 by thetotal number of 10-minute tuning sessions 800 (e.g.,112+242+338+370+390+490+491+781+901+903+945=5963 total sessions) asshown below:

$\frac{112}{5963} = {2\%}$

2% is placed in the conditional distribution bucket for the 0-minutepresentation session 816 within a 10 minute tuning session 800. Otherexample conditional distribution buckets are calculated in a similarmanner. For example, FIG. 8B illustrates that 4% of the 10-minute tuningsessions contain a total presentation time of 1 minute, 8% of the10-minute tuning sessions contain a total presentation time of 5minutes, 16% of the 10-minute tuning sessions contain a totalpresentation time of 10 minutes, etc.

FIG. 8C is an example model of an example cumulative distribution ofpresentation time based on the example conditional distribution of FIG.8B. Additionally other models may be created for conditionaldistribution of presentation time for tuning sessions of varying lengths(e.g., 1-minute session, 5-minute tuning session, 60-minute tuningsession, 720-minute tuning session, etc.) Alternatively, one model maybe generated with multiple rows representing varying tuning sessionlengths.

The example model of FIG. 8C includes an example cumulative distributionof presentation times 824 broken into one minute intervals for anexample 10-minute tuning session 800. In some examples, the presentationtimes 824 represent a range of times. For example, the examplepresentation time ‘0’ labeled 826 may include all time from 00:00:00 to00:00:59, 00:00:00 to 00:00:29, or any other range. The ranges may bepredetermined or may be customized by an administrator. Alternatively,the example cumulative distribution 824 may be broken into any durationof intervals (e.g., thirty second intervals, 2 minute intervals, 5minute intervals, etc.).

Cumulative distribution buckets contain cumulative percentages based onthe conditional distribution of FIG. 8B. The cumulative percentages inthe example cumulative distribution buckets are calculated by adding thepercentage in a selected conditional distribution bucket with thepercentages in all the conditional distribution buckets prior to theselected conditional distribution bucket. For example, the cumulativedistribution bucket for a 3-minute presentation session within a10-minute tuning session 800 is calculated by adding the percentage inthe 3 minute conditional distribution bucket 822 for a 10-minute tuningsession 800 (e.g., 6%) with the percentage in the 2-minute (e.g., 6%)conditional distribution bucket 720, 1-minute (e.g., 4%) conditionaldistribution bucket 818, and 0-minute (e.g., 2%) conditionaldistribution bucket 816 and for a 10-minute tuning session 800 as shownbelow:

6%+6%+4%+2%=18%

18% is placed in the 3-minute cumulative distribution bucket 828 and theother example cumulative distribution buckets are calculated in asimilar manner. The percentages in each cumulative distribution bucketsrepresent the total percentage of presentation times of up to aparticular length of time. For example, 54% of the 10-minute tuningsessions contained a total presentation sessions of 8 minutes or less.Alternatively, a cumulative distribution may be calculated based on thefrequency distribution of FIG. 8A. In this manner, the cumulativedistribution calculated using the frequency distribution of FIG. 8A asappose to the conditional distribution percentages of FIG. 8B. Thedistributions models of FIGS. 8A, 8B, and 8C may be used to adjusttuning data from a STB in order to determine a total presentationsession for the tuning data from the STB, as further described in FIG. 9.

FIG. 8D displays an example model of an example frequency distributionof media output device presented at set times 830 during a 10-minutetuning session 800. Additionally, other models may be created for tuningsessions of varying lengths (e.g., 1-minute tuning session, 5-minutetuning session, 60-minute tuning session, 720-minute tuning session,etc.). Alternatively, one model may be generated with multiple rowsrepresenting the varying tuning session lengths.

The example model of FIG. 8D includes an example frequency distributionof media output devices presented at set times 830 broken into oneminute intervals for an example 10-minute tuning session 800.Alternatively, the frequency distribution 830 may be broken into anyduration of intervals (e.g., thirty second intervals, 2 minuteintervals, 5 minute intervals, etc.). If a user of the mediapresentation device was presentation the media presentation device atthe designated time, the instance is counted in a correspondingfrequency distribution bucket. In some examples, the blocks canrepresent a range of times. For example, the blocks may be broken up sothat if a user was exposed to media by the media presentation device 108within the 00:00:00-00:00:29 window, the instance would be counted inthe ‘at 0’ frequency distribution bucket 832.

To generate and/or update the example model of FIG. 8D, the examplecollection facility 114 of FIG. 1 collects metering data from theexample LPM 106. Once the example data adjuster 120 breaks the meteringdata into tuning sessions and presentation sessions, the example dataadjuster 120 populates the model(s) based on tuning session data andpresentation session data. The example model of the example frequencydistribution of media output devices presenting at set times of FIG. 8Dis populated based on presentation session data from tuning sessions of10 minute length. Each of the 5963 gathered 10-minute tuning sessionsare analyzed to determine how many media presentation device 108 wereactually presenting media on media output device 104 at set times of the10-minute tuning session. For example, a 10-minute tuning sessioncontaining presentation sessions from 00:00-05:30 and from 08:45-10:00would be entered as being watched in the at 0, at 1, at 2, at 3, at 4,at 5, at 9, and at 10 minute frequency distribution blocks.

FIG. 8E displays an example model of an example conditional distributionof media output devices presenting media at set times 836 during a10-minute tuning session 800. Additionally, other models may be createdfor tuning sessions of varying lengths (e.g., 1-minute tuning session,5-minute tuning session, 60-minute tuning session, 720-minute tuningsession, etc.). Alternatively, one model may be generated with multiplerows representing rows representing the varying tuning session lengths.

The example model of FIG. 8E includes an example conditionaldistribution of media output devices presenting media at set times 836broken into one minute intervals for an example 10-minute tuning session800. Alternatively, the conditional distribution at set times 836 may bebroken into any duration of intervals (e.g., thirty second intervals, 2minute intervals, 5 minute intervals, etc.). If a user of the mediapresentation device was presentation the media presentation device atthe designated time, the instance is counted in a correspondingconditional distribution bucket. In some examples, the blocks canrepresent a range of times. For example, the blocks may be broken up sothat if a user was exposed to media by the media presentation device 108within the 00:00:00-00:00:29 window, the instance would be counted inthe ‘at 0’ conditional distribution bucket labeled 838.

Conditional distribution buckets contain conditional percentages basedon the frequency distributions 830 of FIG. 8D. The conditionalpercentages in the example conditional distribution buckets arecalculated based on dividing each frequency distribution bucket by atotal number of tuning sessions modeled for a tuning session length. Forexample, the conditional distribution percentage at the fifth minute forthe example 10 minute-tuning session 800 is calculating by dividing the4411 presentation instances at the fifth minute 834 by the total numberof 10-minute tuning sessions (e.g., 5963 total sessions) as shown below:

$\frac{4411}{5963} = {74\%}$

74% is placed in the at 5 minute conditional bucket 840 for a 10 minutetuning session. Other example conditional distribution buckets 820 arecalculated in a similar manner. The conditional distribution of FIG. 8Billustrates that 100% of the total 10-minute tuning sessions werepresenting media at the zeroth minute, 88% of the total 10-minute tuningsessions were presenting media at the third minute, 49% of the total10-minute tuning sessions were presenting media at the tenth minute,etc. Additionally, a report may be generated including any of theexample models or combination of the example models. The distributionsmodels of FIGS. 8D and 8E may be used to adjust tuning data from a STBbased on an initial presentation session, an ending presentationsession, and/or any other presentation session information for thetuning data of the STB, as previously described in FIG. 4 .

FIG. 9 is an example graph of expected total presentation session valuesbased on various tuning sessions generated from metering data of the LPM106 of FIG. 1 . The example data adjuster 120 determines an expectedtotal presentation session by calculating a weighted average of thetotal presentation sessions of a selected tuning session length. Forexample, as previously described in FIG. 5A, the example expected valuefor the 10-minute tuning session of FIG. 5A was 6.58. Therefore, theexample graph of FIG. 9 will have a coordinate (e.g., (10, 6.58)) torepresent the expected value for the 10-minute tuning session. Theexample graph contains a point for every tracked tuning session (e.g., a1 minute tuning session, 10 minute tuning session, 200 minute tuningsession, etc.). A report may be generated including the example graph.

FIG. 10 is a block diagram of an example processor platform 1000 capableof executing the instructions of FIGS. 3-5 to implement the example dataadjuster 120 of FIG. 1 . The processor platform 1000 can be, forexample, a server, a personal computer, a mobile device (e.g., a cellphone, a smart phone, a tablet such as an iPad™), a personal digitalassistant (PDA), an Internet appliance, or any other type of computingdevice.

The processor platform 1000 of the illustrated example includes aprocessor 1012. The processor 1012 of the illustrated example ishardware. For example, the processor 1012 can be implemented byintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 1012 of the illustrated example includes a local memory1013 (e.g., a cache). The example processor 1012 of FIG. 10 executes theinstructions of FIGS. 3-5 to the example metering receiver 202, theexample tuning session determiner 204, the example presentation sessiondeterminer 206, the example modeler 208, the example model storage 210,the example tuning data receiver 212, the example duration determiner214, the example presentation session estimator 216, the examplereporter 218 of FIG. 2 to implement the example data adjuster 120. Theprocessor 1012 of the illustrated example is in communication with amain memory including a volatile memory 1014 and a non-volatile memory1016 via a bus 1018. The volatile memory 1014 may be implemented bySynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or anyother type of random access memory device. The non-volatile memory 1016may be implemented by flash memory and/or any other desired type ofmemory device. Access to the main memory 1014, 1016 is controlled by amemory controller.

The processor platform 1000 of the illustrated example also includes aninterface circuit 1012. The interface circuit 1012 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1022 are connectedto the interface circuit 1012. The input device(s) 1022 permit(s) a userto enter data and commands into the processor 1012. The input device(s)can be implemented by, for example, a sensor, a microphone, a camera(still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1024 are also connected to the interfacecircuit 1012 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, and/or speakers). The interface circuit 1012 of theillustrated example, thus, typically includes a graphics driver card, agraphics driver circuit or a graphics driver processor.

The interface circuit 1012 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1026 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1000 of the illustrated example also includes oneor more mass storage devices 1028 for storing software and/or data.Examples of such mass storage devices 1028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 1032 of FIGS. 3-5 may be stored in the massstorage device 1028, in the volatile memory 1014, in the non-volatilememory 1016, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it should be appreciated that the above disclosedmethods, apparatus, and articles of manufacture estimate presentationsession from tuning data based on metering data. Media presentationdevice data may have extraneous information leading to inaccurateaudience measurement data. For example, STB data does not account forwhen a television is off and the television is on, or when thetelevision is on, but no one is watching the media presentation device.Methods and apparatus described herein estimate presentation sessionsfor tuning data to account for the extraneous information. Since LPMscan determine more accurate information including when a mediapresentation device is on and when a user is actually watching the mediapresentation device, metering data from the LPM are analyzed to createmodels used to accurately adjust media presentation device data.

Using the examples disclosed herein, media presentation device data maybe more accurately analyzed based on data from a plurality of LPMs. Insome examples, models are created from metering data of LPMs initialpresentation session, a final presentation session, and a totalpresentation session within a tuning session. In such examples,presentation sessions for tuning data from media monitoring devices maybe estimated based on data in corresponding models. In this manner,reports may be generated to include the estimated presentation sessionfor a tuning session of a media presentation device.

From the foregoing, persons of ordinary skill in the art will appreciatethat the above disclosed methods and apparatus may be realized within asingle device or across two cooperating devices, and could beimplemented by software, hardware, and/or firmware to implement the dataadjuster disclosed herein.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe appended claims either literally or under the doctrine ofequivalents.

What is claimed is:
 1. A computing system comprising: machine readable instructions; and processor circuitry to execute the machine readable instructions to at least: access respective tuning session data from a plurality of media presentation devices, corresponding ones of the plurality of media presentation devices in respective ones of media presentation environments, the respective ones of the media presentation environments including at least one media output device to present media from a corresponding one of the media presentation devices, the respective tuning session data from respective ones of the media presentation devices indicative of a tuning session having a tuning session duration, the tuning session duration is indicative of an amount of time between interactions with a corresponding one of the plurality of media presentation devices, presentation session data is available for a first subset of the media presentation environments, presentation session data is not available for a second subset of the media presentation environments; access, for ones of the first subset of the media presentation environments, respective presentation session data indicative of a presentation session duration during which media corresponding to a respective tuning session was presented on the respective at least one media output device by the media presentation device within a respective tuning session duration; generate a model relating the respective tuning session durations to corresponding presentation session durations from ones of the first subset of the media presentation environments; based on the model, determine, for a given one of the media presentation environments in the second subset of the media presentation environments, an expected presentation session duration for a given tuning session having a given tuning session duration; and output the expected presentation session duration for the given tuning session from the given one of the media presentation environments.
 2. The computing system of claim 1, wherein the processor circuitry is to: access, from the first subset of the media presentation environments, additional tuning session data and additional media presentation data indicative of additional tuning session durations and additional media presentation session durations, respectively; and update the model with the additional tuning session durations and additional media presentation data.
 3. The computing system of claim 2, wherein the processor circuitry is to: subsequent to determining the expected the presentation session duration, based on the model, for the given tuning session having the given tuning session duration, determine, for another given one of the media presentation environments in the second subset of the media presentation environments, another expected presentation session duration for another given tuning session having another given tuning session duration based on the updated model; and output the other expected presentation session duration for the other given tuning session from the other given one of the media presentation environments.
 4. The computing system of claim 1, wherein the processor circuitry is to determine the expected presentation session duration for the given tuning session based on the model by determining the expected presentation session duration based on multiple ones of the tuning session durations and corresponding ones of the presentation session durations of the ones of the first subset of the media presentation environments, the multiple ones of the tuning session durations being within a threshold extent of similarity of the given tuning session duration.
 5. The computing system of claim 4, wherein the threshold extent of similarity is a threshold of time similarity in which each of the tuning session durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments are within a time threshold of the given tuning session duration.
 6. The computing system of claim 5, wherein the expected presentation session duration for the given presentation session is a weighted average of the presentation session durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments.
 7. The computing system of claim 5, wherein the time threshold is four minutes.
 8. The computing system of claim 4, wherein the expected presentation session duration for the given presentation session is a conditional probability of the presentation session durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments, indicated by the model.
 9. The computing system of claim 1, wherein the processor circuitry is to: determine that a number of tuning session durations related by the model to corresponding presentation session durations from the first subset of the media presentation environments is at least a threshold number of tuning session durations, and determine the expected presentation session duration for the given tuning session having the given tuning session duration based on the model in response to the determination that the number of tuning session durations related by the model to corresponding presentation session durations from the first subset of the media presentation environments is at least the threshold number of tuning session durations.
 10. The computing system of claim 9, wherein the threshold number is five hundred tuning session durations.
 11. A non-transitory computer-readable medium comprising instructions to cause processor circuitry to at least: access respective tuning session data from each of a plurality of media presentation devices, corresponding ones of the media presentation devices in respective ones of media presentation environments, the respective media presentation environments including at least one media output device to present media from a corresponding one of the media presentation devices, the respective tuning session data from respective ones of the media presentation devices indicative of a tuning session having a tuning session duration, the tuning session duration is indicative of an amount of time between interactions with a corresponding one of the media presentation devices, presentation session data is available for a first subset of the media presentation environments, and presentation session data is not available for a second subset of the media presentation environments; access, for ones of the first subset of the media presentation environments, respective presentation session data indicative of a presentation session duration during which media corresponding to a respective tuning session was presented by the respective at least one media output device by the media presentation device within a respective tuning session duration; generate a model relating the respective tuning session durations to corresponding presentation session durations from the ones of the first subset of the media presentation environments; based on the model, determine, for a given one of the media presentation environments in the second subset of the media presentation environments, an expected presentation session duration for a given tuning session having a given tuning session duration; and output the expected presentation session duration for the given tuning session from the given one of the media presentation environments.
 12. The non-transitory computer-readable medium of claim 11, wherein the instructions cause the processor circuitry to determine the expected presentation session duration, based on the model, for the given tuning session by determining the expected presentation session duration based on multiple ones of the tuning session durations and corresponding ones of the presentation session durations of the ones of the first subset of the media presentation environments, the multiple ones of the tuning session durations being within a threshold extent of similarity of the given tuning session duration, wherein the threshold extent of similarity is a threshold of time similarity in which each of the tuning session durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments are within a time threshold of the given tuning session duration, and wherein the expected presentation session duration for the given presentation session is a weighted average of the presentation session durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments.
 13. The non-transitory computer-readable medium of claim 11, wherein the instructions cause the processor circuitry to determine the expected presentation session duration for the given tuning session based on the model by determining the expected presentation session duration based on multiple ones of the tuning session durations and corresponding ones of the presentation session durations of the ones of the first subset of the media presentation environments, the multiple ones of the tuning session durations being within a threshold extent of similarity of the given tuning session duration, and wherein the expected presentation session duration for the given presentation session is a conditional probability of the presentation session durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments.
 14. The non-transitory computer-readable medium of claim 11, the instructions cause the processor circuitry to: determine that a number of tuning session durations related by the model to corresponding presentation session durations from the first subset of the media presentation environments is at least a threshold number of tuning session durations, and determine the expected presentation session duration for the given tuning session having the given tuning session duration based on the model in response to the determination that the number of tuning session durations related by the model to corresponding presentation session durations from the first subset of the media presentation environments is at least the threshold number of tuning session durations.
 15. A system comprising: processor circuitry; and a computer-readable medium including instructions to cause the processor circuitry to at least: access respective tuning session data from a plurality of set-top boxes, corresponding ones of the set-top boxes in respective ones of media presentation environments, the respective media presentation environments including at least one television to present media from a corresponding one of the set-top boxes, the respective tuning session data from respective ones of the set-top boxes indicative of a tuning session having a tuning session duration, the tuning session duration indicative of an amount of time between interactions with a corresponding one of the set-top boxes, on/off durations are available for a first subset of the media presentation environments, on/off durations are not available for a second subset of the media presentation environments; access, for ones of the first subset of the media presentation environments, respective on/off durations corresponding to respective tuning sessions, the on/off duration indicative of an amount of time that the respective television was turned on during the respective tuning session; generate a model relating the respective tuning session durations to corresponding on/off durations from the ones of the first subset of the media presentation environments; based on the model, determine, for a given one of the media presentation environments in the second subset of the media presentation environments, an expected on/off duration for a given tuning session having a given tuning session duration; and cause presentation of the expected on/off duration for the given tuning session from the given one of the media presentation environments.
 16. The system of claim 15, wherein the processor circuitry is to: access, from additional ones of the first subset of the media presentation environments, additional tuning session data indicative of additional tuning session durations and additional on/off durations indicative of an amount of time that the respective television was turned on during the additional tuning session; and update the model with the additional tuning session durations and additional on/off durations.
 17. The system of claim 16, wherein the processor circuitry is to: subsequent to determining the expected on/off duration, based on the model, for the given tuning session having the given tuning session duration, determine, for another given one of the media presentation environments in the second subset of the media presentation environments, another expected on/off duration for another given tuning session having another given tuning session duration based on the updated model; and output the other expected on/off duration for the other given tuning session from the other given one of the media presentation environments.
 18. The system of claim 15, wherein the processor circuitry is to determine the expected on/off duration for the given tuning session based on the model by determining the expected on/off duration based on multiple ones of the tuning session durations and corresponding ones of the on/off durations of the ones of the first subset of the media presentation environments, the multiple ones of the tuning session durations being within a threshold extent of similarity of the given tuning session duration.
 19. The system of claim 18, wherein the threshold extent of similarity is a threshold of time similarity in which each of the tuning session durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments are within a time threshold of the given tuning session duration.
 20. The system of claim 19, wherein the expected on/off duration for the given tuning session is a weighted average of the on/off durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments.
 21. The system of claim 18, wherein the expected on/off duration for the given tuning session is a conditional probability of the on/off durations of the multiple ones of the tuning session durations of the ones of the first subset of the media presentation environments, indicated by the model.
 22. The system of claim 15, wherein the processor circuitry is to: determine that a number of tuning session durations related by the model to corresponding on/off durations from the first subset of the media presentation environments is at least a threshold number of tuning session durations, and determine the expected on/off duration for the given tuning session having the given tuning session duration based on the model in response to the determination that the number of tuning session durations related by the model to corresponding on/off durations from the first subset of the media presentation environments is at least the threshold number of tuning session durations.
 23. The system of claim 22, wherein the threshold number is five hundred tuning session durations. 